Integrating ML in experimental pipelines - Gemma Turon

This talk will focus on the implementation of ML models to actual experimental pipelines. We will review strategies for sharing pre-trained models that can be readily adopted by non-expert users, and thow to bridge the gap between dry-lab and wet-lab researchers, with case studies in the field of biomedicine. The interactive tutorial will exploit one of such pretrained open source model hub repositories, the Ersilia Model Hub.

Talk by Gemma Turon.
Trained as a molecular biologist, Gemma completed a PhD in colorectal cancer and stem cells at IRB Barcelona in 2019, before taking a one-year break to focus on working and volunteering in the third sector. This shifted her scientific interest to global health and neglected diseases, and the existing barriers to tackle some of the most urgent health issues in developing countries. With Ersilia, she aims to explore new ways of community building and engagement in the scientific arena, at the intersection between academia, biotech start-ups and NPOs.

This talk was part of the workshop "Real-world Perspectives to Avoid the Worst Mistakes using Machine Learning in Science" at Pydata Global 2022, organised by Jesper Dramsch, Gemma Turon, and ValerioMaggio.

The workshop has received funding from the Software Sustainability Institute through the 2022 fellowship programme received by Jesper Dramsch.
https://dramsch.net/ssi

Find the programme of the workshop, transcripts and more resources here:
https://realworld-ml.xyz/

#MachineLearning #PydataGlobal

Видео Integrating ML in experimental pipelines - Gemma Turon канала Jesper Dramsch – Non-hype Machine Learning